An Empirical Study on the Procedure to Derive Software Quality Estimation Models

TL;DR

This paper proposes a comprehensive procedure combining statistical, machine learning, and neuro-fuzzy techniques to derive and improve software quality estimation models using data from the ISBSG repository.

Contribution

It introduces a general, empirically validated procedure for developing software quality estimation models with multiple advanced techniques.

Findings

Neuro-fuzzy approach enhances estimation accuracy.

Statistical and machine learning techniques effectively verify software metrics.

Empirical validation confirms the procedure's effectiveness.

Abstract

Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value.